Direct Image-to-Likelihood for Track-Before-Detect Multi-Bernoulli Filter
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Author(s)
Murphy, Timothy S.
Holzinger, Marcus J.
Flewelling, Brien R.
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Abstract
This paper aims to apply the random finite set-based multi-Bernoilli filter to frame to-
frame tracking of space objects observed in electro optical imagery for space
domain awareness applications. First, this paper will review random finite set filters
applied to frame to frame tracking and their applications to space. A new likelihood
function for space based imagery will be presented, based on the matched
filter. A more educated birth model will be proposed which better models potential
SO using observer characteristics and object dynamics. Simulation results
will explore the range of objects that can be tracked. The final algorithm is able to
perform completely uncued detection down to a total object SNR of 5.6 and a per
pixel SNR of 1.5. Promising but inconclusive results are shown for total object
SNR of 3.35 and per pixel SNR of 0.7.
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2016-02
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